class_docstr: Logistic Regression classifier.
  Parameters:
  -----------
  learning_rate: float
      The step length that will be taken when following the negative gradient during
      training.
  gradient_descent: boolean
      True or false depending if gradient descent should be used when training. If
      false then we use batch optimization by least squares.
code: def predict(self, X):
      y_pred = np.round(self.sigmoid(X.dot(self.param))).astype(int)
      return y_pred
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/logistic_regression.py
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import make_diagonal, Plot
  from mlfromscratch.deep_learning.activation_functions import Sigmoid
label_desc: Use image node_id 4 for calling the LogisticRegression obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred